{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:reagent.core.dataclasses:USE_VANILLA_DATACLASS: False\n",
      "INFO:reagent.core.dataclasses:ARBITRARY_TYPES_ALLOWED: True\n",
      "INFO:reagent.core.registry_meta:Adding REGISTRY to type LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Not Registering LearningRateSchedulerConfig to LearningRateSchedulerConfig. Abstract method [] are not implemented.\n",
      "INFO:reagent.core.registry_meta:Registering LambdaLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering MultiplicativeLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering StepLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering MultiStepLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering ExponentialLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering CosineAnnealingLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering CyclicLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering OneCycleLR to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Registering CosineAnnealingWarmRestarts to LearningRateSchedulerConfig\n",
      "INFO:reagent.core.registry_meta:Adding REGISTRY to type OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Not Registering OptimizerConfig to OptimizerConfig. Abstract method [] are not implemented.\n",
      "INFO:reagent.core.registry_meta:Registering Adam to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering AdamW to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering SparseAdam to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering Adamax to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering LBFGS to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering Rprop to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering ASGD to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering Adadelta to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering Adagrad to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering RMSprop to OptimizerConfig\n",
      "INFO:reagent.core.registry_meta:Registering SGD to OptimizerConfig\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "import torch\n",
    "from typing import Iterable, Mapping, Optional, Sequence, Set, Tuple, Union\n",
    "from reagent.ope.estimators.sequential_estimators import (\n",
    "    Mdp,\n",
    "    Model,\n",
    "    RLPolicy,\n",
    "    State,\n",
    "    StateReward,\n",
    "    Transition,\n",
    "    ActionSpace,\n",
    "    ActionDistribution,\n",
    "    Action,\n",
    "    RandomRLPolicy,\n",
    "    RLEstimatorInput,\n",
    "    IPSEstimator,\n",
    "    NeuralDualDICE,\n",
    ")\n",
    "from reagent.models.dqn import FullyConnectedDQN\n",
    "from reagent.ope.utils import Clamper, RunningAverage\n",
    "from gym import wrappers\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "NUM_EPISODES = 200\n",
    "MAX_HORIZON = 250\n",
    "GAMMA = 0.99\n",
    "ALPHA = 0.66\n",
    "\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else None\n",
    "print(f\"Device - {device}\")\n",
    "\n",
    "model = torch.jit.load(\"/mnt/vol/gfsfblearner-nebraska/flow/data/2020-07-24/18eeebdf-b0ed-4f93-b079-95f7c58656ff/207187922_207187922_0.pt\")\n",
    "model = model.dqn_with_preprocessor.model\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the policy classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ComboPolicy(RLPolicy):\n",
    "    # Weighted combination between two given policies\n",
    "    def __init__(self, action_space: ActionSpace, weights: Sequence[float], policies: Sequence[RLPolicy]):\n",
    "        assert len(weights) == len(policies)\n",
    "        self._weights = weights\n",
    "        self._policies = policies\n",
    "        self._action_space = action_space\n",
    "        self._softmax = torch.nn.Softmax()\n",
    "    \n",
    "    def action_dist(self, state: State) -> ActionDistribution:\n",
    "        weighted_policies = [w * p(state).values for w,p in zip(self._weights, self._policies)]\n",
    "        weighted = torch.stack(weighted_policies).sum(0)\n",
    "        dist = self._softmax(weighted)\n",
    "        return self._action_space.distribution(dist)\n",
    "    \n",
    "class PyTorchPolicy(RLPolicy):\n",
    "    def __init__(self, action_space: ActionSpace, model):\n",
    "        self._action_space = action_space\n",
    "        self._model = model\n",
    "        self._softmax = torch.nn.Softmax()\n",
    "        \n",
    "    def action_dist(self, state: State) -> ActionDistribution:\n",
    "        dist = self._model(torch.tensor(state.value, dtype=torch.float).reshape(1, -1))[0]\n",
    "        return self._action_space.distribution(self._softmax(dist))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_logs(episodes: int, max_horizon: int, policy: RLPolicy) -> Sequence[Mdp]:\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        episodes: number of episodes to generate\n",
    "        max_horizon: max horizon of each episode\n",
    "        policy: RLPolicy which uses real-valued states\n",
    "    \"\"\"\n",
    "    log = []\n",
    "    env = gym.make('CartPole-v0')\n",
    "    for _ in range(episodes):\n",
    "        init_state = env.reset()\n",
    "        cur_state = init_state\n",
    "        mdp = []\n",
    "        for _ in range(max_horizon):\n",
    "            action_dist = policy(State(cur_state))\n",
    "            action = action_dist.greedy().value\n",
    "            action_prob = action_dist.probability(Action(action))\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            mdp.append(Transition(last_state=State(cur_state),\n",
    "                                 action=Action(action),\n",
    "                                 action_prob=action_prob,\n",
    "                                 state=State(next_state),\n",
    "                                 reward=reward,\n",
    "                                 status=2 if done else 1))\n",
    "            if done:\n",
    "                break\n",
    "            cur_state = next_state\n",
    "        log.append(mdp)\n",
    "    return log\n",
    "\n",
    "def zeta_nu_loss_callback(losses: Sequence[Tuple[float, float]], \n",
    "                          estimated_values: Sequence, \n",
    "                          input: RLEstimatorInput):\n",
    "    def callback_fn(zeta_loss, nu_loss, estimator):\n",
    "        losses.append((zeta_loss, nu_loss))\n",
    "        estimated_values.append(estimator._compute_estimates(input))\n",
    "    return callback_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the trained policy, target policy, and behavior policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_policy = RandomRLPolicy(ActionSpace(2))\n",
    "model_policy = PyTorchPolicy(ActionSpace(2), model)\n",
    "target_policy = ComboPolicy(ActionSpace(2), [1.0, 0.0], [model_policy, random_policy])\n",
    "behavior_policy = ComboPolicy(ActionSpace(2), [0.55 + 0.15 * ALPHA, 0.45 - 0.15 * ALPHA], [model_policy, random_policy])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate the logged dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "log = generate_logs(NUM_EPISODES, MAX_HORIZON, behavior_policy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate the value of the target policy\n",
    "\n",
    "Since the states are real-valued, instead of estimating v^pi(s), we take the average sum of the discounted rewards over numerous trials, getting E[v^pi(s)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Policy Ground Truth value: 70.20302794198436\n"
     ]
    }
   ],
   "source": [
    "def estimate_value(episodes: int, max_horizon: int, policy: RLPolicy, gamma: float):\n",
    "    avg = RunningAverage()\n",
    "    env = gym.make('CartPole-v0')\n",
    "    for _ in range(episodes):\n",
    "        init_state = env.reset()\n",
    "        cur_state = init_state\n",
    "        r = 0.0\n",
    "        discount = 1.0\n",
    "        for _ in range(max_horizon):\n",
    "            action_dist = policy(State(cur_state))\n",
    "            action = action_dist.greedy().value\n",
    "            action_prob = action_dist.probability(Action(action))\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            r += reward * discount\n",
    "            discount *= gamma\n",
    "            if done:\n",
    "                break\n",
    "            cur_state = next_state\n",
    "        avg.add(r)\n",
    "    return avg.average\n",
    "\n",
    "ground_truth = estimate_value(NUM_EPISODES, MAX_HORIZON, target_policy, GAMMA)\n",
    "print(f\"Target Policy Ground Truth value: {ground_truth}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = RLEstimatorInput(\n",
    "    gamma=GAMMA,\n",
    "    log=log,\n",
    "    target_policy=target_policy,\n",
    "    discrete_states=False\n",
    ")\n",
    "ips = IPSEstimator()\n",
    "dualdice_losses = []\n",
    "dualdice_values = []\n",
    "dualdice = NeuralDualDICE(4, 2, deterministic_env=True, \n",
    "                          value_lr=0.003, zeta_lr=0.003, \n",
    "                          batch_size=2048, \n",
    "                          loss_callback_fn=zeta_nu_loss_callback(dualdice_losses, dualdice_values, inp),\n",
    "                          device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:IPSEstimator(device(None),weighted[True]}: start evaluating\n",
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "/home/alexschneidman/anaconda3/envs/ope/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  del sys.path[0]\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=74.5090560913086, ground_truth=0.0\n",
      "INFO:root:IPSEstimator(device(None),weighted[True]}: finishing evaluating[process_time=13.853707919000001]\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=12.197612311945937, ground_truth=0.0\n",
      "INFO:root:Samples 100 Avg Zeta Loss 0.013515950131695717, Avg Value Loss -0.011872679508778674\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=21.359412562633842, ground_truth=0.0\n",
      "INFO:root:Samples 200 Avg Zeta Loss 0.032867668516701073, Avg Value Loss -0.03195237421035925\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=31.3605478482464, ground_truth=0.0\n",
      "INFO:root:Samples 300 Avg Zeta Loss 0.06170809593284501, Avg Value Loss -0.060989961180688\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=39.15435264085474, ground_truth=0.0\n",
      "INFO:root:Samples 400 Avg Zeta Loss 0.09260961384687108, Avg Value Loss -0.09186012931436383\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=45.733648655608356, ground_truth=0.0\n",
      "INFO:root:Samples 500 Avg Zeta Loss 0.1208919502585195, Avg Value Loss -0.12021297005033559\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=50.04632369489927, ground_truth=0.0\n",
      "INFO:root:Samples 600 Avg Zeta Loss 0.14566879029812604, Avg Value Loss -0.14500885449528747\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.906834703138784, ground_truth=0.0\n",
      "INFO:root:Samples 700 Avg Zeta Loss 0.16704220785193952, Avg Value Loss -0.16637350306068183\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=56.61997176190003, ground_truth=0.0\n",
      "INFO:root:Samples 800 Avg Zeta Loss 0.18543581553356497, Avg Value Loss -0.18476388545841008\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=59.48113522645389, ground_truth=0.0\n",
      "INFO:root:Samples 900 Avg Zeta Loss 0.20143835243743122, Avg Value Loss -0.20076695910877684\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=60.22765881056284, ground_truth=0.0\n",
      "INFO:root:Samples 1000 Avg Zeta Loss 0.21566343501652593, Avg Value Loss -0.21490484686303174\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=58.97033364185004, ground_truth=0.0\n",
      "INFO:root:Samples 1100 Avg Zeta Loss 0.22887356189566418, Avg Value Loss -0.22799524105505548\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=56.226421100993456, ground_truth=0.0\n",
      "INFO:root:Samples 1200 Avg Zeta Loss 0.2419627257225026, Avg Value Loss -0.24107862580657052\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.761702546247356, ground_truth=0.0\n",
      "INFO:root:Samples 1300 Avg Zeta Loss 0.25531347974328894, Avg Value Loss -0.25432005140726405\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.73503067061738, ground_truth=0.0\n",
      "INFO:root:Samples 1400 Avg Zeta Loss 0.269031892017561, Avg Value Loss -0.26795893059321824\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.137122485773936, ground_truth=0.0\n",
      "INFO:root:Samples 1500 Avg Zeta Loss 0.28323989188966014, Avg Value Loss -0.2821323165006636\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=52.98327834499681, ground_truth=0.0\n",
      "INFO:root:Samples 1600 Avg Zeta Loss 0.29787298655413924, Avg Value Loss -0.2967181593279537\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.43967350268721, ground_truth=0.0\n",
      "INFO:root:Samples 1700 Avg Zeta Loss 0.31294792548958755, Avg Value Loss -0.31169310430456576\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=52.67908842175486, ground_truth=0.0\n",
      "INFO:root:Samples 1800 Avg Zeta Loss 0.32836873602781735, Avg Value Loss -0.3270895791804788\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.315209816077974, ground_truth=0.0\n",
      "INFO:root:Samples 1900 Avg Zeta Loss 0.34415020323346207, Avg Value Loss -0.3427792751559455\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.24424501362402, ground_truth=0.0\n",
      "INFO:root:Samples 2000 Avg Zeta Loss 0.3601947005562248, Avg Value Loss -0.3587538495441672\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.68376994087084, ground_truth=0.0\n",
      "INFO:root:Samples 2100 Avg Zeta Loss 0.37643962734012987, Avg Value Loss -0.3749142044007209\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.96217747478104, ground_truth=0.0\n",
      "INFO:root:Samples 2200 Avg Zeta Loss 0.39291921264947005, Avg Value Loss -0.391323937455849\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.75942171227504, ground_truth=0.0\n",
      "INFO:root:Samples 2300 Avg Zeta Loss 0.4095673371546738, Avg Value Loss -0.40792873051550044\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.67920215574946, ground_truth=0.0\n",
      "INFO:root:Samples 2400 Avg Zeta Loss 0.4264367160840384, Avg Value Loss -0.424742956217825\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.3068904221308, ground_truth=0.0\n",
      "INFO:root:Samples 2500 Avg Zeta Loss 0.44355532330451514, Avg Value Loss -0.4417917512242315\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.40253030984578, ground_truth=0.0\n",
      "INFO:root:Samples 2600 Avg Zeta Loss 0.46088527779336663, Avg Value Loss -0.45900153291381285\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.589157551630656, ground_truth=0.0\n",
      "INFO:root:Samples 2700 Avg Zeta Loss 0.47835447261894665, Avg Value Loss -0.4763657887835853\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.81791241682344, ground_truth=0.0\n",
      "INFO:root:Samples 2800 Avg Zeta Loss 0.49603770180630297, Avg Value Loss -0.4939257093469584\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.64493523722547, ground_truth=0.0\n",
      "INFO:root:Samples 2900 Avg Zeta Loss 0.513904861388005, Avg Value Loss -0.511616965743344\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.820140496441205, ground_truth=0.0\n",
      "INFO:root:Samples 3000 Avg Zeta Loss 0.5319195063228248, Avg Value Loss -0.5295093464904623\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.66295844959422, ground_truth=0.0\n",
      "INFO:root:Samples 3100 Avg Zeta Loss 0.5500221946272967, Avg Value Loss -0.5475205865265477\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.61842681610751, ground_truth=0.0\n",
      "INFO:root:Samples 3200 Avg Zeta Loss 0.5682244423883818, Avg Value Loss -0.5656256978636238\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=55.44779874188362, ground_truth=0.0\n",
      "INFO:root:Samples 3300 Avg Zeta Loss 0.5865773189171554, Avg Value Loss -0.5838913073010155\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=53.71785597261784, ground_truth=0.0\n",
      "INFO:root:Samples 3400 Avg Zeta Loss 0.6050301781923317, Avg Value Loss -0.6021948606524044\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.10404156323258, ground_truth=0.0\n",
      "INFO:root:Samples 3500 Avg Zeta Loss 0.6237087326147589, Avg Value Loss -0.6207745987733435\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.0615611733165, ground_truth=0.0\n",
      "INFO:root:Samples 3600 Avg Zeta Loss 0.6424753768340448, Avg Value Loss -0.6394050202480294\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=54.56301885083942, ground_truth=0.0\n",
      "INFO:root:Samples 3700 Avg Zeta Loss 0.6613085941780935, Avg Value Loss -0.6580782256298456\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=55.461626653172125, ground_truth=0.0\n",
      "INFO:root:Samples 3800 Avg Zeta Loss 0.680294641233968, Avg Value Loss -0.6769630713253901\n",
      "INFO:root:  Append estimate [1]: log=69.98554447789161, estimated=55.62284399474573, ground_truth=0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Samples 3900 Avg Zeta Loss 0.6993606929072077, Avg Value Loss -0.695912910153744\n",
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      "INFO:root:Samples 4000 Avg Zeta Loss 0.7185064421679705, Avg Value Loss -0.7149408297876721\n",
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      "INFO:root:Samples 4100 Avg Zeta Loss 0.7377599143193148, Avg Value Loss -0.7340898682761199\n",
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      "INFO:root:Samples 4200 Avg Zeta Loss 0.7571012823719949, Avg Value Loss -0.753317346207699\n",
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      "INFO:root:Samples 4300 Avg Zeta Loss 0.7765199167863462, Avg Value Loss -0.7726287578741903\n",
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      "INFO:root:Samples 4400 Avg Zeta Loss 0.7960622947944043, Avg Value Loss -0.7920336581938382\n",
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      "INFO:root:Samples 4500 Avg Zeta Loss 0.8156998060090562, Avg Value Loss -0.8115298611358543\n",
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      "INFO:root:Samples 4600 Avg Zeta Loss 0.8354063662313138, Avg Value Loss -0.8310887727305692\n",
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      "INFO:root:Samples 4700 Avg Zeta Loss 0.855160370210056, Avg Value Loss -0.850693606090902\n",
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      "INFO:root:Samples 4800 Avg Zeta Loss 0.8750115939797729, Avg Value Loss -0.8704083379639694\n",
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      "INFO:root:Samples 4900 Avg Zeta Loss 0.894942492326598, Avg Value Loss -0.8901813413204962\n",
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      "INFO:root:Samples 5000 Avg Zeta Loss 0.9149067743608257, Avg Value Loss -0.9099908599408633\n",
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      "INFO:root:Samples 5100 Avg Zeta Loss 0.9349380812992556, Avg Value Loss -0.9298498113513938\n",
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      "INFO:root:Samples 5200 Avg Zeta Loss 0.9549997544125205, Avg Value Loss -0.9497817114255531\n",
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      "INFO:root:Samples 5300 Avg Zeta Loss 0.9751475657446674, Avg Value Loss -0.9697622633010495\n",
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      "INFO:root:Samples 5400 Avg Zeta Loss 0.995287306513766, Avg Value Loss -0.9897496287057586\n",
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      "INFO:root:Samples 5500 Avg Zeta Loss 1.0155125500351097, Avg Value Loss -1.0098306881326768\n",
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      "INFO:root:Samples 5600 Avg Zeta Loss 1.0357304478391378, Avg Value Loss -1.0299038212668479\n",
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      "INFO:root:Samples 6000 Avg Zeta Loss 1.1171735915201308, Avg Value Loss -1.1107215389437302\n",
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      "INFO:root:Samples 7000 Avg Zeta Loss 1.323255518833005, Avg Value Loss -1.3152916751271346\n",
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      "INFO:root:Samples 8000 Avg Zeta Loss 1.534261463571887, Avg Value Loss -1.5246660846789784\n",
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     ]
    },
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     "output_type": "stream",
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      "INFO:root:Samples 8200 Avg Zeta Loss 1.5770045495220326, Avg Value Loss -1.567101864874857\n",
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      "INFO:root:Samples 8700 Avg Zeta Loss 1.6847023614149887, Avg Value Loss -1.6741258043274638\n",
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      "INFO:root:Samples 8800 Avg Zeta Loss 1.7063623882598042, Avg Value Loss -1.6956090021964343\n",
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      "INFO:root:Samples 10000 Avg Zeta Loss 1.969276748171883, Avg Value Loss -1.9567217556254104\n",
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     ]
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      "INFO:root:Samples 12400 Avg Zeta Loss 2.5044362658385912, Avg Value Loss -2.4879692679271863\n",
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     ]
    },
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     ]
    },
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     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "ips_result = ips.evaluate(inp)\n",
    "dd_result = dualdice.evaluate(inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dualdice_losses(losses):\n",
    "    zeta_losses = [x[0] for x in losses]\n",
    "    nu_losses = [x[1] for x in losses]\n",
    "    plt.plot(zeta_losses, label=\"Zeta Loss\")\n",
    "    plt.plot(nu_losses, label=\"Nu Loss\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.show()\n",
    "\n",
    "plot_dualdice_losses(dualdice_losses)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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